ZEROTOP: Zero-Shot Task-Oriented Semantic Parsing using Large Language Models

Dheeraj Mekala, Jason Wolfe, Subhro Roy


Abstract
We explore the use of large language models (LLMs) for zero-shot semantic parsing. Semantic parsing involves mapping natural language utterances to task-specific meaning representations. LLMs are generally trained on publicly available text and code and cannot be expected to directly generalize to domain-specific parsing tasks in a zero-shot setting. In this work, we propose ZEROTOP, a zero-shot task-oriented parsing method that decomposes semantic parsing problem into a set of abstractive and extractive question-answering (QA) problems. For each utterance, we prompt the LLM with questions corresponding to its top-level intent and a set of slots and use the LLM generations to construct the target meaning representation. We observe that current LLMs fail to detect unanswerable questions; and as a result, cannot handle questions corresponding to missing slots. We address this by fine-tuning a language model on public QA datasets using synthetic negative samples. Experimental results show that our QA-based decomposition paired with the fine-tuned LLM can zero-shot parse 16% of utterances in the MTOP dataset.
Anthology ID:
2023.emnlp-main.354
Volume:
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2023
Address:
Singapore
Editors:
Houda Bouamor, Juan Pino, Kalika Bali
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5792–5799
Language:
URL:
https://aclanthology.org/2023.emnlp-main.354
DOI:
10.18653/v1/2023.emnlp-main.354
Bibkey:
Cite (ACL):
Dheeraj Mekala, Jason Wolfe, and Subhro Roy. 2023. ZEROTOP: Zero-Shot Task-Oriented Semantic Parsing using Large Language Models. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 5792–5799, Singapore. Association for Computational Linguistics.
Cite (Informal):
ZEROTOP: Zero-Shot Task-Oriented Semantic Parsing using Large Language Models (Mekala et al., EMNLP 2023)
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PDF:
https://preview.aclanthology.org/ingest-acl-2023-videos/2023.emnlp-main.354.pdf